What the data for this category looks like
The data for this category mainly comes from the national public resource trading platform, natural resource trading systems, property right exchange systems, and state-owned asset supervision systems. The update frequency is daily. Daily documents include transfer announcements, trading instructions, bid application materials, margin vouchers, transaction confirmation letters, listing result announcements, and property right transfer specifications. These documents typically contain professional fields such as ownership number, trading target type, margin amount, and listing starting price. Units include square meters, ten thousand yuan, and lots.
Constraints on model access and configuration
Decentralized data sources and daily updates require access logic that supports incremental synchronization across multiple systems. This avoids full-volume data pulling that consumes excessive computing resources. Diverse document types and professional ownership, amount fields require chunking rules adapted to different document lengths. This ensures professional fields are not accidentally truncated. Ownership information is directly linked to transaction compliance. When calling the model, the context range must be strictly limited. This prevents irrelevant data from causing errors in ownership judgment.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
maxContext | 8000–12000 token | Matches the context length of long documents such as property right transfer specifications, to avoid truncating core ownership information |
maxTokens | 2000–3000 token | Controls the reply length for party building Q&A, to ensure complete coverage of trading rules and ownership explanations |
quoteMaxToken | 1500 token | Limits the length of quoted trading document fragments, to ensure quoted content is accurate and does not exceed context limits |
temperature | 0.2–0.4 | Reduces randomness in party building Q&A, to ensure accurate expression of professional content such as ownership and trading processes |
vision | Disabled | Core documents for this category are mainly structured text, with no image understanding requirements. This reduces resource consumption |
AIPROXY_API_ENDPOINT | Fill in the dedicated endpoint according to the deployment environment | Adapts to domestically compliant API call paths, to ensure data transmission meets security requirements for public resource trading |
AIPROXY_API_TOKEN | Fill in the dedicated key assigned by the platform | Ensures valid identity verification for API calls, to prevent requests from being intercepted or rejected |
Three common configuration errors
- A test call to the channel link returns the error "undefined" is not valid JSON: The configured
AIPROXY_API_ENDPOINTdoes not correctly adapt to the deployment environment, leading to abnormal API request formatting. - Accidentally enabling the
visionparameter and attempting to configure an image understanding model: Core documents for this category are mainly structured text, with no need for image parsing capabilities. This results in redundant configuration. - Incorrectly setting
toolChoiceto forced tool calling: The party building Q&A scenario does not require calling external tools. Forced tool calling causes workflow interruptions and returns invalid results.
How to confirm the configuration is complete
- Perform an incremental synchronization test, review the data update log, and confirm that daily incremental data is pulled normally.
- Input party building-related questions about transaction compliance, and check whether the reply accurately references core fields such as ownership and trading rules.
- Initiate a test request via the channel link, and confirm that the returned result format is valid JSON with no abnormal errors.
- Check the
visionparameter status, confirm that it is disabled and no image parsing tasks are triggered, to reduce unnecessary resource consumption.
The values provided are common starting points and should be evaluated against the samples used in actual deployment.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.